Deep Learning is a subset of Machine Learning that empowers systems to learn from vast amounts of data. Using neural networks, deep learning has led to significant advancements in areas such as computer vision, natural language processing, and speech recognition.
Before diving into deep learning, you should have a firm grasp on the following concepts:
- Linear Algebra
- Vectors, Matrices, Eigenvalues, and Eigenvectors.
- Calculus
- Derivatives, Gradients, Chain Rule, and Partial Derivatives.
- Probability and Statistics
- Probability Distributions, Bayes’ Theorem, Statistical Inference.
- Proficiency in Python, the primary language for deep learning.
- Familiarity with libraries like NumPy and Pandas for data manipulation.
- Understanding supervised and unsupervised learning.
- Knowledge of overfitting, underfitting, and evaluation metrics like accuracy, precision, recall, and F1 score.
- Understanding the structure of a neural network (layers, nodes, activation functions).
- Learning about forward propagation and backpropagation.
- Feedforward Neural Networks (FNN) 🖼️
- Basic neural networks with no feedback loop.
- Convolutional Neural Networks (CNN) 🖼️
- Used in image processing tasks like image classification and object detection.
- Recurrent Neural Networks (RNN) 🖼️
- Best for sequence data like time series and text.
- Long Short-Term Memory Networks (LSTM) 🖼️
- A specialized form of RNN designed to handle long-range dependencies.
- TensorFlow 🐍 [Link]
- An open-source machine learning platform.
- PyTorch 🔥 [Link]
- A dynamic deep learning library loved by researchers.
- Dropout: Randomly drops neurons during training to prevent overfitting.
- Batch Normalization: Normalizes inputs to layers within the network to stabilize training and improve speed.
- Understand key optimization algorithms:
- Stochastic Gradient Descent (SGD) ✏️
- Adam Optimizer 🧮
- RMSProp
- Tuning hyperparameters using Grid Search or Random Search.
- Fine-tuning pre-trained models like ResNet, VGG, and BERT for new tasks with limited data.
- A type of neural network used to learn efficient representations of data, typically for the purpose of dimensionality reduction.
- Used for generating new data samples that resemble the training data. GANs have been used for image generation, super-resolution, and style transfer.
- "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville [Link]
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron [Link]
- Kaggle: Join competitions and engage with other learners [Link]
- Deep Learning Reddit Community: Stay updated with the latest research and trends [Link]
- Tasks like image classification, object detection, and segmentation.
- Example: Self-driving cars, facial recognition systems.
- Tasks like sentiment analysis, text generation, and language translation.
- Example: Virtual assistants, machine translation systems.
- Converting spoken language into text.
- Example: Google Assistant, Alexa, and Siri.
Deep learning is a rapidly evolving field with immense potential in various industries. By following this roadmap, you will build a strong foundation in deep learning concepts and techniques. Keep learning, experimenting, and contributing to the community!